# computation of BLUP's, using seedling dry weight data as example
library(lme4)
d <- read.table('SeedlingDryWeight2.txt',as.is=T, header=T)
# create factors
d$g <- as.factor(d$Genotype)
d$t <- as.factor(d$Tray)
d$s <- as.factor(d$Seedling)
# fit the mixed effects model model
temp <- lmer(SeedlingWeight~g+(1|t),data=d)
# BLUPs
blup <- ranef(temp)
# look at ranef() output to see its structure
str(blup)
# there are options to ranef(drop=T) to simplify structure of output
# compare BLUP to genotype averages
gmean <- tapply(d$SeedlingWeight,d$t,mean)
plot(gmean,blup$t$'(Intercept)')
# BLUPs + fixed effects
# there is no predict() for an lmer object
# these are the equivalents
betau <- coef(temp)
# values are beta + u for each row
trayX <- cbind(rep(1,8), rep(c(0,1),c(4,4)))
# model.matrix with one row per tray
pred.tray <- apply(betau$t*trayX,1,sum)
plot(gmean, pred.tray)